More than 60 million people have been infected with the human immunodeficiency virus (“HIV”), the causative agent of acquired immune deficiency syndrome (“AIDS”), since the early 1980s. See Lucas, 2002, Lepr Rev. 73(1):64-71. HIV/AIDS is now the leading cause of death in sub-Saharan Africa, and is the fourth biggest killer worldwide. At the end of 2001, an estimated 40 million people were living with HIV globally. See Norris, 2002, Radiol Technol. 73(4):339-363.
Modern anti-HIV drugs target different stages of the HIV life cycle and a variety of enzymes essential for HIV's replication and/or survival. Amongst the drugs that have so far been approved for AIDS therapy are nucleoside reverse transcriptase inhibitors such as AZT, ddI, ddC, d4T, 3TC, abacavir, nucleotide reverse transcriptase inhibitors such as tenofovir, non-nucleoside reverse transcriptase inhibitors such as nevirapine, efavirenz, delavirdine and protease inhibitors such as saquinavir, ritonavir, indinavir, nelfinavir, amprenavir and lopinavir.
One consequence of the action of an anti-viral drug is that it can exert sufficient selective pressure on virus replication to select for drug-resistant mutants (Herrmann et al., 1977, Ann NY Acad Sci 284:632-637). With increasing drug exposure, the selective pressure on the replicating virus population increases to promote the more rapid emergence of drug resistant mutants.
With the inevitable emergence of drug resistance, strategies must be designed to optimize treatment in the face of resistant virus populations. Ascertaining the contribution of drug resistance to drug failure is difficult because patients that are likely to develop drug resistance are also likely to have other factors that predispose them to a poor prognosis (Richman, 1994, AIDS Res Hum Retroviruses 10:901-905). In addition, each patient typically harbors a diverse mixture of mutant strains of the virus with different mutant strains having different susceptibilities to anti-viral drugs.
The traditional tools available to assess anti-viral drug resistance are inadequate; the classical tests for determining the resistance of HIV to an anti-viral agent are complex, time-consuming, expensive, potentially hazardous and not custom tailored to the treatment of a given patient. See Barre-Sinoussi et al., 1983, Science 220:868-871; Popovic et al., 1984, Science 224:497-500), and variations of it (see, e.g., Goedert et al., 1987, JAMA 257:331-334; Allain et al., 1987, N. Engl. J. Med. 317:1114-1121; Piatak et al., 1993, Science 259:1749-1754; Urdea, 1993, Clin. Chem. 39:725-726; Kellam and Larder, 1994, Antimicrobial Agents and Chemo. 38:23-30.
Two general approaches are now used for measuring resistance to anti-viral drugs. The first, called phenotypic testing, directly measures the susceptibility of virus taken from an infected person's virus to particular anti-viral drugs. Petropoulos et al., 2000, Antimicrob. Agents Chemother. 44:920-928 and Hertogs et al., 1998, Antimicrob Agents Chemother 42(2):269-76 provide a description of phenotypic assays in widespread use today. Gunthard et al., 1998, AIDS Res Hum Retroviruses 14:869-76 and Schuurman et al., 1999, J Clin Microbiol. 37:2291-96 discuss currently prevalent genotypic assays. Hirsch et al., 2000, JAMA 283:2417-26 provide a general analysis of the currently available assays for testing drug susceptibility.
The second method, called genotypic testing, detects mutations in the virus that affect drug susceptibility and can associate specific genetic mutations with drug resistance and drug failure. Genotypic testing examines virus taken from a patient, looking for the presence of specific genetic mutations that are associated with resistance to certain drugs. Genotypic testing has a few advantages over phenotypic testing, most notably the relative simplicity and speed with which the test can be performed. The testing can take as little as a few days to complete, and because it is less complex, it is somewhat cheaper to perform. However, interpretation of genotypic data is dependent on previous knowledge of the relationships between specific mutations and changes in drug susceptibility.
Carrillo et al., 1998, J. Virol. 72:7532-41 describe the in vitro selection and characterization of HIV-1 variants having reduced susceptibility to lopinavir. Nine different mutations at 8 amino acid positions were associated with reduced susceptibility to lopinavir. A subsequent study found 23 different mutations at 11 positions in the HIV protease that correlated with reduced in vitro susceptibility to lopinavir in plasma samples of HIV-infected patients who had been treated previously with at least one protease inhibitor (Kempf et al., 2001, J. Virol. 75:7462-69). A crude algorithm that attempted to correlate the phenotypic resistance to lopinavir with the number of mutations observed at the 11 identified positions, and therefore to predict the effectiveness of lopinavir treatment, was postulated (Kempf et al., 2000, Antiviral Therapy 5 (suppl. 3):70, abstract 89). According to the algorithm, a virus was susceptible to treatment with lopinavir if it had five or fewer mutations at the 11 identified positions in its protease. If the number of mutations at these 11 positions was six or more, then the virus was predicted to be resistant to lopinavir treatment. Id.
Efforts to date to use genotypic correlates of reduced susceptibility to predict the effectiveness of anti-viral drugs, especially drugs targeted against the ever-evolving HIV are, at best, imperfect. An algorithm that can more accurately predict whether a given anti-viral drug or combination of drugs would be effective in treating a given patient would save time and money by identifying drugs that are not likely to succeed before they are administered to the patient. More importantly, it would improve the quality of life of the patient by sparing him or her the trauma of treatment with potent toxins that result in no improvement with respect to his or her HIV infection. Therefore, an urgent need exists for a more accurate algorithm for predicting whether a particular drug would be effective for treating a particular patient. Moreover, a genotype based assay can be faster and more cost effective than phenotypic assays.